code: def sample_min_n_steps_from_buffer(
      replay_buffer: ReplayBuffer,
      min_steps: int,
      count_by_agent_steps: bool,
  ) -> Optional[SampleBatchType]:
      """Samples a minimum of n timesteps from a given replay buffer.
  
      This utility method is primarily used by the QMIX algorithm and helps with
      sampling a given number of time steps which has stored samples in units
      of sequences or complete episodes. Samples n batches from replay buffer
      until the total number of timesteps reaches `train_batch_size`.
  
      Args:
          replay_buffer: The replay buffer to sample from
          num_timesteps: The number of timesteps to sample
          count_by_agent_steps: Whether to count agent steps or env steps
  
      Returns:
          A concatenated SampleBatch or MultiAgentBatch with samples from the
          buffer.
      """
      train_batch_size = 0
      train_batches = []
      while train_batch_size < min_steps:
          batch = replay_buffer.sample(num_items=1)
          batch_len = (
              batch.agent_steps()
              if count_by_agent_steps
              else batch.env_steps()
          )
          if batch_len == 0:
              # Replay has not started, so we can't accumulate timesteps here
              return batch
          train_batches.append(batch)
          train_batch_size += batch_len
      # All batch types are the same type, hence we can use any concat_samples()
      train_batch = concat_samples(train_batches)
      return train_batch
  
file_path: ray/rllib/utils/replay_buffers/utils.py
imports: import logging
  import psutil
  from typing import Optional, Any
  import numpy
  from ray.rllib.utils import deprecation_warning
  from ray.rllib.utils.annotations import DeveloperAPI
  from ray.rllib.utils.deprecation import DEPRECATED_VALUE
  from ray.rllib.utils.from_config import from_config
  from ray.rllib.utils.metrics.learner_info import LEARNER_STATS_KEY
  from ray.rllib.utils.replay_buffers import MultiAgentPrioritizedReplayBuffer, ReplayBuffer, MultiAgentReplayBuffer
  from ray.rllib.policy.sample_batch import concat_samples, MultiAgentBatch, SampleBatch
  from ray.rllib.utils.typing import ResultDict, SampleBatchType, AlgorithmConfigDict
  from ray.util import log_once
label_desc: Use image node_id 2 for calling a global function with example usage: sample_min_n_steps_from_buffer(replay_buffer, min_steps, count_by_agent_steps) and returns: train_batch, batch